Overview

Dataset statistics

Number of variables26
Number of observations14606
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory208.0 B

Variable types

Categorical8
Numeric17
Boolean1

Alerts

id has a high cardinality: 14606 distinct values High cardinality
date_activ has a high cardinality: 1796 distinct values High cardinality
date_end has a high cardinality: 368 distinct values High cardinality
date_modif_prod has a high cardinality: 2129 distinct values High cardinality
date_renewal has a high cardinality: 386 distinct values High cardinality
cons_12m is highly correlated with cons_last_month and 2 other fieldsHigh correlation
cons_gas_12m is highly correlated with nb_prod_actHigh correlation
cons_last_month is highly correlated with cons_12m and 2 other fieldsHigh correlation
forecast_cons_12m is highly correlated with cons_12m and 3 other fieldsHigh correlation
forecast_cons_year is highly correlated with cons_last_month and 3 other fieldsHigh correlation
forecast_meter_rent_12m is highly correlated with forecast_price_energy_off_peak and 3 other fieldsHigh correlation
forecast_price_energy_off_peak is highly correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_energy_peak is highly correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_pow_off_peak is highly correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
imp_cons is highly correlated with cons_last_month and 3 other fieldsHigh correlation
margin_gross_pow_ele is highly correlated with margin_net_pow_eleHigh correlation
margin_net_pow_ele is highly correlated with margin_gross_pow_eleHigh correlation
nb_prod_act is highly correlated with cons_gas_12mHigh correlation
net_margin is highly correlated with cons_12m and 3 other fieldsHigh correlation
pow_max is highly correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
cons_12m is highly correlated with cons_last_monthHigh correlation
cons_gas_12m is highly correlated with cons_last_monthHigh correlation
cons_last_month is highly correlated with cons_12m and 1 other fieldsHigh correlation
forecast_cons_12m is highly correlated with forecast_cons_year and 2 other fieldsHigh correlation
forecast_cons_year is highly correlated with forecast_cons_12m and 1 other fieldsHigh correlation
forecast_meter_rent_12m is highly correlated with forecast_price_energy_off_peak and 2 other fieldsHigh correlation
forecast_price_energy_off_peak is highly correlated with forecast_meter_rent_12m and 1 other fieldsHigh correlation
forecast_price_energy_peak is highly correlated with forecast_meter_rent_12mHigh correlation
forecast_price_pow_off_peak is highly correlated with forecast_price_energy_off_peakHigh correlation
imp_cons is highly correlated with forecast_cons_12m and 1 other fieldsHigh correlation
margin_gross_pow_ele is highly correlated with margin_net_pow_eleHigh correlation
margin_net_pow_ele is highly correlated with margin_gross_pow_eleHigh correlation
net_margin is highly correlated with forecast_cons_12mHigh correlation
pow_max is highly correlated with forecast_meter_rent_12mHigh correlation
cons_12m is highly correlated with cons_last_month and 2 other fieldsHigh correlation
cons_gas_12m is highly correlated with nb_prod_actHigh correlation
cons_last_month is highly correlated with cons_12m and 2 other fieldsHigh correlation
forecast_cons_12m is highly correlated with cons_12m and 1 other fieldsHigh correlation
forecast_cons_year is highly correlated with cons_last_month and 1 other fieldsHigh correlation
forecast_price_energy_off_peak is highly correlated with forecast_price_pow_off_peakHigh correlation
forecast_price_energy_peak is highly correlated with forecast_price_pow_off_peak and 1 other fieldsHigh correlation
forecast_price_pow_off_peak is highly correlated with forecast_price_energy_off_peak and 2 other fieldsHigh correlation
imp_cons is highly correlated with cons_last_month and 1 other fieldsHigh correlation
margin_gross_pow_ele is highly correlated with margin_net_pow_eleHigh correlation
margin_net_pow_ele is highly correlated with margin_gross_pow_eleHigh correlation
nb_prod_act is highly correlated with cons_gas_12mHigh correlation
net_margin is highly correlated with cons_12m and 1 other fieldsHigh correlation
pow_max is highly correlated with forecast_price_energy_peak and 1 other fieldsHigh correlation
cons_12m is highly correlated with cons_gas_12m and 1 other fieldsHigh correlation
cons_gas_12m is highly correlated with cons_12m and 1 other fieldsHigh correlation
cons_last_month is highly correlated with cons_12m and 1 other fieldsHigh correlation
forecast_cons_12m is highly correlated with forecast_cons_year and 4 other fieldsHigh correlation
forecast_cons_year is highly correlated with forecast_cons_12m and 1 other fieldsHigh correlation
forecast_discount_energy is highly correlated with forecast_cons_12m and 3 other fieldsHigh correlation
forecast_meter_rent_12m is highly correlated with forecast_price_energy_off_peak and 3 other fieldsHigh correlation
forecast_price_energy_off_peak is highly correlated with forecast_discount_energy and 6 other fieldsHigh correlation
forecast_price_energy_peak is highly correlated with forecast_discount_energy and 3 other fieldsHigh correlation
forecast_price_pow_off_peak is highly correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
imp_cons is highly correlated with forecast_cons_12m and 2 other fieldsHigh correlation
margin_gross_pow_ele is highly correlated with forecast_price_energy_off_peak and 1 other fieldsHigh correlation
margin_net_pow_ele is highly correlated with forecast_price_energy_off_peak and 1 other fieldsHigh correlation
net_margin is highly correlated with forecast_cons_12m and 2 other fieldsHigh correlation
num_years_antig is highly correlated with forecast_price_energy_off_peak and 2 other fieldsHigh correlation
origin_up is highly correlated with num_years_antigHigh correlation
pow_max is highly correlated with forecast_cons_12m and 1 other fieldsHigh correlation
net_margin is highly skewed (γ1 = 36.56951466) Skewed
id is uniformly distributed Uniform
id has unique values Unique
cons_gas_12m has 11994 (82.1%) zeros Zeros
cons_last_month has 4983 (34.1%) zeros Zeros
forecast_cons_12m has 306 (2.1%) zeros Zeros
forecast_cons_year has 6148 (42.1%) zeros Zeros
forecast_discount_energy has 14094 (96.5%) zeros Zeros
forecast_meter_rent_12m has 725 (5.0%) zeros Zeros
forecast_price_energy_peak has 7021 (48.1%) zeros Zeros
imp_cons has 6169 (42.2%) zeros Zeros
margin_gross_pow_ele has 157 (1.1%) zeros Zeros
margin_net_pow_ele has 157 (1.1%) zeros Zeros
net_margin has 185 (1.3%) zeros Zeros

Reproduction

Analysis started2022-04-05 18:18:58.078452
Analysis finished2022-04-05 18:22:32.811110
Duration3 minutes and 34.73 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct14606
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
303740e6ba50bbb6aca61dbe1b0994f5
 
1
abcfc174050f9dabbccfb52de2db7417
 
1
508f77b2738091ae41b7bcda284416b3
 
1
7bbe17ea1417fd9e84287b291194c925
 
1
ca3b1f3d0959fd8fc6bb8d690ce2409e
 
1
Other values (14601)
14601 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters467392
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14606 ?
Unique (%)100.0%

Sample

1st row24011ae4ebbe3035111d65fa7c15bc57
2nd rowd29c2c54acc38ff3c0614d0a653813dd
3rd row764c75f661154dac3a6c254cd082ea7d
4th rowbba03439a292a1e166f80264c16191cb
5th row149d57cf92fc41cf94415803a877cb4b

Common Values

ValueCountFrequency (%)
303740e6ba50bbb6aca61dbe1b0994f51
 
< 0.1%
abcfc174050f9dabbccfb52de2db74171
 
< 0.1%
508f77b2738091ae41b7bcda284416b31
 
< 0.1%
7bbe17ea1417fd9e84287b291194c9251
 
< 0.1%
ca3b1f3d0959fd8fc6bb8d690ce2409e1
 
< 0.1%
d44b5c7227d9cd0188a6d5871e1cbf3f1
 
< 0.1%
cf99781308d7f93fa1d7fde6e49467ee1
 
< 0.1%
e46bb9a2dc590624ecd7c29e2b2d2c1c1
 
< 0.1%
47d4e820f1ef653c4007eb43ce32ecd01
 
< 0.1%
9dce82a4ac2d7af52e593d484e935ba71
 
< 0.1%
Other values (14596)14596
99.9%

Length

2022-04-05T15:22:33.295182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91a15ff32a1886a3e2d481b2810397791
 
< 0.1%
3837fee9f948dca6612c45bef8b2583d1
 
< 0.1%
a8e9d5d93381eb69d8149595a7d73a181
 
< 0.1%
bad4892ef67fbdfbb53c4139900a266e1
 
< 0.1%
9296a3ca343aca9bf4bc6a971a8c7f761
 
< 0.1%
f46e1995fdd251b8b4118e10c7d2bbf61
 
< 0.1%
c86963c89f24623d72567b06d9c2debe1
 
< 0.1%
1f9547c3c7a8aabda46907526972147c1
 
< 0.1%
29f9c53daea90ad1365ba36ab1f6b2221
 
< 0.1%
5747d5d77fcbf6a77eae04adc6d998921
 
< 0.1%
Other values (14596)14596
99.9%

Most occurring characters

ValueCountFrequency (%)
629453
 
6.3%
e29432
 
6.3%
729400
 
6.3%
429381
 
6.3%
229375
 
6.3%
b29313
 
6.3%
329297
 
6.3%
129257
 
6.3%
c29210
 
6.2%
a29169
 
6.2%
Other values (6)174105
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number292239
62.5%
Lowercase Letter175153
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
629453
10.1%
729400
10.1%
429381
10.1%
229375
10.1%
329297
10.0%
129257
10.0%
829153
10.0%
029090
10.0%
929012
9.9%
528821
9.9%
Lowercase Letter
ValueCountFrequency (%)
e29432
16.8%
b29313
16.7%
c29210
16.7%
a29169
16.7%
d29137
16.6%
f28892
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common292239
62.5%
Latin175153
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
629453
10.1%
729400
10.1%
429381
10.1%
229375
10.1%
329297
10.0%
129257
10.0%
829153
10.0%
029090
10.0%
929012
9.9%
528821
9.9%
Latin
ValueCountFrequency (%)
e29432
16.8%
b29313
16.7%
c29210
16.7%
a29169
16.7%
d29137
16.6%
f28892
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII467392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
629453
 
6.3%
e29432
 
6.3%
729400
 
6.3%
429381
 
6.3%
229375
 
6.3%
b29313
 
6.3%
329297
 
6.3%
129257
 
6.3%
c29210
 
6.2%
a29169
 
6.2%
Other values (6)174105
37.3%

channel_sales
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
foosdfpfkusacimwkcsosbicdxkicaua
6754 
MISSING
3725 
lmkebamcaaclubfxadlmueccxoimlema
1843 
usilxuppasemubllopkaafesmlibmsdf
1375 
ewpakwlliwisiwduibdlfmalxowmwpci
893 
Other values (3)
 
16

Length

Max length32
Median length32
Mean length25.62419554
Min length7

Characters and Unicode

Total characters374267
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfoosdfpfkusacimwkcsosbicdxkicaua
2nd rowMISSING
3rd rowfoosdfpfkusacimwkcsosbicdxkicaua
4th rowlmkebamcaaclubfxadlmueccxoimlema
5th rowMISSING

Common Values

ValueCountFrequency (%)
foosdfpfkusacimwkcsosbicdxkicaua6754
46.2%
MISSING3725
25.5%
lmkebamcaaclubfxadlmueccxoimlema1843
 
12.6%
usilxuppasemubllopkaafesmlibmsdf1375
 
9.4%
ewpakwlliwisiwduibdlfmalxowmwpci893
 
6.1%
sddiedcslfslkckwlfkdpoeeailfpeds11
 
0.1%
epumfxlbckeskwekxbiuasklxalciiuu3
 
< 0.1%
fixdbufsefwooaasfcxdxadsiekoceaa2
 
< 0.1%

Length

2022-04-05T15:22:33.776569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-05T15:22:34.115705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
foosdfpfkusacimwkcsosbicdxkicaua6754
46.2%
missing3725
25.5%
lmkebamcaaclubfxadlmueccxoimlema1843
 
12.6%
usilxuppasemubllopkaafesmlibmsdf1375
 
9.4%
ewpakwlliwisiwduibdlfmalxowmwpci893
 
6.1%
sddiedcslfslkckwlfkdpoeeailfpeds11
 
0.1%
epumfxlbckeskwekxbiuasklxalciiuu3
 
< 0.1%
fixdbufsefwooaasfcxdxadsiekoceaa2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a35415
 
9.5%
c35313
 
9.4%
s33465
 
8.9%
i29355
 
7.8%
f25792
 
6.9%
k24420
 
6.5%
o24390
 
6.5%
u22226
 
5.9%
m21883
 
5.8%
d18573
 
5.0%
Other values (11)103435
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter348192
93.0%
Uppercase Letter26075
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a35415
10.2%
c35313
10.1%
s33465
9.6%
i29355
 
8.4%
f25792
 
7.4%
k24420
 
7.0%
o24390
 
7.0%
u22226
 
6.4%
m21883
 
6.3%
d18573
 
5.3%
Other values (6)77360
22.2%
Uppercase Letter
ValueCountFrequency (%)
I7450
28.6%
S7450
28.6%
M3725
14.3%
N3725
14.3%
G3725
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin374267
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a35415
 
9.5%
c35313
 
9.4%
s33465
 
8.9%
i29355
 
7.8%
f25792
 
6.9%
k24420
 
6.5%
o24390
 
6.5%
u22226
 
5.9%
m21883
 
5.8%
d18573
 
5.0%
Other values (11)103435
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII374267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a35415
 
9.5%
c35313
 
9.4%
s33465
 
8.9%
i29355
 
7.8%
f25792
 
6.9%
k24420
 
6.5%
o24390
 
6.5%
u22226
 
5.9%
m21883
 
5.8%
d18573
 
5.0%
Other values (11)103435
27.6%

cons_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11065
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159220.2863
Minimum0
Maximum6207104
Zeros117
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:34.804081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1512.25
Q15674.75
median14115.5
Q340763.75
95-th percentile913771.75
Maximum6207104
Range6207104
Interquartile range (IQR)35089

Descriptive statistics

Standard deviation573465.2642
Coefficient of variation (CV)3.601709793
Kurtosis42.68977714
Mean159220.2863
Median Absolute Deviation (MAD)10669
Skewness5.997308122
Sum2325571501
Variance3.288624092 × 1011
MonotonicityNot monotonic
2022-04-05T15:22:35.435638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0117
 
0.8%
288259727
 
0.2%
332924424
 
0.2%
620710418
 
0.1%
174302518
 
0.1%
392606018
 
0.1%
172217917
 
0.1%
228883817
 
0.1%
250392316
 
0.1%
96328816
 
0.1%
Other values (11055)14318
98.0%
ValueCountFrequency (%)
0117
0.8%
12
 
< 0.1%
22
 
< 0.1%
34
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
620710418
0.1%
573144814
0.1%
53224411
 
< 0.1%
51614564
 
< 0.1%
49394874
 
< 0.1%
440652014
0.1%
43066569
0.1%
41994905
 
< 0.1%
410037913
0.1%
40125642
 
< 0.1%

cons_gas_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2112
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28092.37533
Minimum0
Maximum4154590
Zeros11994
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:36.118322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile75854
Maximum4154590
Range4154590
Interquartile range (IQR)0

Descriptive statistics

Standard deviation162973.0591
Coefficient of variation (CV)5.801327128
Kurtosis126.3336345
Mean28092.37533
Median Absolute Deviation (MAD)0
Skewness9.59752999
Sum410317234
Variance2.656021798 × 1010
MonotonicityNot monotonic
2022-04-05T15:22:36.752052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011994
82.1%
97673127
 
0.2%
86792124
 
0.2%
4153218
 
0.1%
195938618
 
0.1%
119241417
 
0.1%
47541316
 
0.1%
46836915
 
0.1%
133705614
 
0.1%
18757813
 
0.1%
Other values (2102)2450
 
16.8%
ValueCountFrequency (%)
011994
82.1%
117
 
< 0.1%
122
 
< 0.1%
212
 
< 0.1%
321
 
< 0.1%
352
 
< 0.1%
361
 
< 0.1%
411
 
< 0.1%
432
 
< 0.1%
461
 
< 0.1%
ValueCountFrequency (%)
41545902
 
< 0.1%
28130192
 
< 0.1%
20550982
 
< 0.1%
195938618
0.1%
18600524
 
< 0.1%
18594913
 
< 0.1%
18139431
 
< 0.1%
17119301
 
< 0.1%
16539242
 
< 0.1%
15428671
 
< 0.1%

cons_last_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4751
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16090.26975
Minimum0
Maximum771203
Zeros4983
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:37.411442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median792.5
Q33383
95-th percentile82161.5
Maximum771203
Range771203
Interquartile range (IQR)3383

Descriptive statistics

Standard deviation64364.19642
Coefficient of variation (CV)4.000193745
Kurtosis47.76299141
Mean16090.26975
Median Absolute Deviation (MAD)792.5
Skewness6.391406975
Sum235014480
Variance4142749781
MonotonicityNot monotonic
2022-04-05T15:22:38.065088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04983
34.1%
38264727
 
0.2%
50982624
 
0.2%
55812018
 
0.1%
46921018
 
0.1%
10616118
 
0.1%
18118717
 
0.1%
23704417
 
0.1%
5428116
 
0.1%
31301816
 
0.1%
Other values (4741)9452
64.7%
ValueCountFrequency (%)
04983
34.1%
113
 
0.1%
25
 
< 0.1%
35
 
< 0.1%
44
 
< 0.1%
55
 
< 0.1%
62
 
< 0.1%
75
 
< 0.1%
85
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
77120314
0.1%
7607271
 
< 0.1%
6122471
 
< 0.1%
55812018
0.1%
50982624
0.2%
50759814
0.1%
4790309
 
0.1%
4698982
 
< 0.1%
46921018
0.1%
4564625
 
< 0.1%

date_activ
Categorical

HIGH CARDINALITY

Distinct1796
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
2009-08-01
 
95
2010-02-01
 
92
2009-09-01
 
76
2009-10-01
 
55
2012-02-01
 
48
Other values (1791)
14240 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters146060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique452 ?
Unique (%)3.1%

Sample

1st row2013-06-15
2nd row2009-08-21
3rd row2010-04-16
4th row2010-03-30
5th row2010-01-13

Common Values

ValueCountFrequency (%)
2009-08-0195
 
0.7%
2010-02-0192
 
0.6%
2009-09-0176
 
0.5%
2009-10-0155
 
0.4%
2012-02-0148
 
0.3%
2010-01-1148
 
0.3%
2010-01-1847
 
0.3%
2011-12-1545
 
0.3%
2010-06-1042
 
0.3%
2011-11-2341
 
0.3%
Other values (1786)14017
96.0%

Length

2022-04-05T15:22:38.705117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2009-08-0195
 
0.7%
2010-02-0192
 
0.6%
2009-09-0176
 
0.5%
2009-10-0155
 
0.4%
2012-02-0148
 
0.3%
2010-01-1148
 
0.3%
2010-01-1847
 
0.3%
2011-12-1545
 
0.3%
2010-06-1042
 
0.3%
2011-11-2341
 
0.3%
Other values (1786)14017
96.0%

Most occurring characters

ValueCountFrequency (%)
040175
27.5%
-29212
20.0%
127567
18.9%
226396
18.1%
95098
 
3.5%
34311
 
3.0%
62750
 
1.9%
82725
 
1.9%
52632
 
1.8%
42625
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116848
80.0%
Dash Punctuation29212
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040175
34.4%
127567
23.6%
226396
22.6%
95098
 
4.4%
34311
 
3.7%
62750
 
2.4%
82725
 
2.3%
52632
 
2.3%
42625
 
2.2%
72569
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-29212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common146060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040175
27.5%
-29212
20.0%
127567
18.9%
226396
18.1%
95098
 
3.5%
34311
 
3.0%
62750
 
1.9%
82725
 
1.9%
52632
 
1.8%
42625
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII146060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040175
27.5%
-29212
20.0%
127567
18.9%
226396
18.1%
95098
 
3.5%
34311
 
3.0%
62750
 
1.9%
82725
 
1.9%
52632
 
1.8%
42625
 
1.8%

date_end
Categorical

HIGH CARDINALITY

Distinct368
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
2016-02-01
 
145
2016-08-01
 
125
2016-09-01
 
117
2016-10-05
 
115
2016-12-31
 
104
Other values (363)
14000 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters146060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2016-06-15
2nd row2016-08-30
3rd row2016-04-16
4th row2016-03-30
5th row2016-03-07

Common Values

ValueCountFrequency (%)
2016-02-01145
 
1.0%
2016-08-01125
 
0.9%
2016-09-01117
 
0.8%
2016-10-05115
 
0.8%
2016-12-31104
 
0.7%
2016-10-25103
 
0.7%
2016-09-0293
 
0.6%
2016-06-1080
 
0.5%
2016-06-0179
 
0.5%
2016-03-0178
 
0.5%
Other values (358)13567
92.9%

Length

2022-04-05T15:22:39.197382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-02-01145
 
1.0%
2016-08-01125
 
0.9%
2016-09-01117
 
0.8%
2016-10-05115
 
0.8%
2016-12-31104
 
0.7%
2016-10-25103
 
0.7%
2016-09-0293
 
0.6%
2016-06-1080
 
0.5%
2016-06-0179
 
0.5%
2016-11-1278
 
0.5%
Other values (358)13567
92.9%

Most occurring characters

ValueCountFrequency (%)
033124
22.7%
-29212
20.0%
127832
19.1%
222902
15.7%
616275
11.1%
73421
 
2.3%
33291
 
2.3%
52549
 
1.7%
82536
 
1.7%
92494
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116848
80.0%
Dash Punctuation29212
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033124
28.3%
127832
23.8%
222902
19.6%
616275
13.9%
73421
 
2.9%
33291
 
2.8%
52549
 
2.2%
82536
 
2.2%
92494
 
2.1%
42424
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-29212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common146060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033124
22.7%
-29212
20.0%
127832
19.1%
222902
15.7%
616275
11.1%
73421
 
2.3%
33291
 
2.3%
52549
 
1.7%
82536
 
1.7%
92494
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII146060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033124
22.7%
-29212
20.0%
127832
19.1%
222902
15.7%
616275
11.1%
73421
 
2.3%
33291
 
2.3%
52549
 
1.7%
82536
 
1.7%
92494
 
1.7%

date_modif_prod
Categorical

HIGH CARDINALITY

Distinct2129
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
2015-11-01
 
721
2015-05-24
 
269
2015-12-17
 
199
2015-09-20
 
173
2015-12-16
 
172
Other values (2124)
13072 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters146060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)3.8%

Sample

1st row2015-11-01
2nd row2009-08-21
3rd row2010-04-16
4th row2010-03-30
5th row2010-01-13

Common Values

ValueCountFrequency (%)
2015-11-01721
 
4.9%
2015-05-24269
 
1.8%
2015-12-17199
 
1.4%
2015-09-20173
 
1.2%
2015-12-16172
 
1.2%
2015-11-18164
 
1.1%
2015-07-11158
 
1.1%
2015-04-29155
 
1.1%
2015-08-27150
 
1.0%
2015-10-30149
 
1.0%
Other values (2119)12296
84.2%

Length

2022-04-05T15:22:39.689976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-11-01721
 
4.9%
2015-05-24269
 
1.8%
2015-12-17199
 
1.4%
2015-09-20173
 
1.2%
2015-12-16172
 
1.2%
2015-11-18164
 
1.1%
2015-07-11158
 
1.1%
2015-04-29155
 
1.1%
2015-08-27150
 
1.0%
2015-10-30149
 
1.0%
Other values (2119)12296
84.2%

Most occurring characters

ValueCountFrequency (%)
036132
24.7%
-29212
20.0%
129112
19.9%
223330
16.0%
57646
 
5.2%
44177
 
2.9%
34096
 
2.8%
93851
 
2.6%
73055
 
2.1%
62959
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116848
80.0%
Dash Punctuation29212
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036132
30.9%
129112
24.9%
223330
20.0%
57646
 
6.5%
44177
 
3.6%
34096
 
3.5%
93851
 
3.3%
73055
 
2.6%
62959
 
2.5%
82490
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-29212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common146060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036132
24.7%
-29212
20.0%
129112
19.9%
223330
16.0%
57646
 
5.2%
44177
 
2.9%
34096
 
2.8%
93851
 
2.6%
73055
 
2.1%
62959
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII146060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036132
24.7%
-29212
20.0%
129112
19.9%
223330
16.0%
57646
 
5.2%
44177
 
2.9%
34096
 
2.8%
93851
 
2.6%
73055
 
2.1%
62959
 
2.0%

date_renewal
Categorical

HIGH CARDINALITY

Distinct386
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
2015-06-23
 
587
2015-03-09
 
451
2015-02-09
 
273
2015-07-04
 
265
2015-10-11
 
231
Other values (381)
12799 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters146060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.5%

Sample

1st row2015-06-23
2nd row2015-08-31
3rd row2015-04-17
4th row2015-03-31
5th row2015-03-09

Common Values

ValueCountFrequency (%)
2015-06-23587
 
4.0%
2015-03-09451
 
3.1%
2015-02-09273
 
1.9%
2015-07-04265
 
1.8%
2015-10-11231
 
1.6%
2015-06-14220
 
1.5%
2015-03-06203
 
1.4%
2015-06-08148
 
1.0%
2015-05-23140
 
1.0%
2015-07-09137
 
0.9%
Other values (376)11951
81.8%

Length

2022-04-05T15:22:40.183031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-06-23587
 
4.0%
2015-03-09451
 
3.1%
2015-02-09273
 
1.9%
2015-07-04265
 
1.8%
2015-10-11231
 
1.6%
2015-06-14220
 
1.5%
2015-03-06203
 
1.4%
2015-06-08148
 
1.0%
2015-05-23140
 
1.0%
2015-07-09137
 
0.9%
Other values (376)11951
81.8%

Most occurring characters

ValueCountFrequency (%)
032467
22.2%
-29212
20.0%
127367
18.7%
223246
15.9%
515212
10.4%
63964
 
2.7%
33857
 
2.6%
93178
 
2.2%
43033
 
2.1%
82302
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116848
80.0%
Dash Punctuation29212
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032467
27.8%
127367
23.4%
223246
19.9%
515212
13.0%
63964
 
3.4%
33857
 
3.3%
93178
 
2.7%
43033
 
2.6%
82302
 
2.0%
72222
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
-29212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common146060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032467
22.2%
-29212
20.0%
127367
18.7%
223246
15.9%
515212
10.4%
63964
 
2.7%
33857
 
2.6%
93178
 
2.2%
43033
 
2.1%
82302
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII146060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032467
22.2%
-29212
20.0%
127367
18.7%
223246
15.9%
515212
10.4%
63964
 
2.7%
33857
 
2.6%
93178
 
2.2%
43033
 
2.1%
82302
 
1.6%

forecast_cons_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13993
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1868.61488
Minimum0
Maximum82902.83
Zeros306
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:41.066126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84.9175
Q1494.995
median1112.875
Q32401.79
95-th percentile6127.095
Maximum82902.83
Range82902.83
Interquartile range (IQR)1906.795

Descriptive statistics

Standard deviation2387.571531
Coefficient of variation (CV)1.277722637
Kurtosis147.4266807
Mean1868.61488
Median Absolute Deviation (MAD)752.955
Skewness7.155852616
Sum27292988.93
Variance5700497.814
MonotonicityNot monotonic
2022-04-05T15:22:41.662299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0306
 
2.1%
0.156
 
< 0.1%
415.144
 
< 0.1%
0.33
 
< 0.1%
651.213
 
< 0.1%
1210.673
 
< 0.1%
0.453
 
< 0.1%
335.53
 
< 0.1%
1539.373
 
< 0.1%
442.743
 
< 0.1%
Other values (13983)14269
97.7%
ValueCountFrequency (%)
0306
2.1%
0.11
 
< 0.1%
0.156
 
< 0.1%
0.181
 
< 0.1%
0.21
 
< 0.1%
0.33
 
< 0.1%
0.321
 
< 0.1%
0.331
 
< 0.1%
0.422
 
< 0.1%
0.453
 
< 0.1%
ValueCountFrequency (%)
82902.831
< 0.1%
61357.171
< 0.1%
48412.581
< 0.1%
35789.291
< 0.1%
35312.211
< 0.1%
32174.471
< 0.1%
31347.111
< 0.1%
30533.991
< 0.1%
28375.761
< 0.1%
27618.391
< 0.1%

forecast_cons_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4218
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1399.762906
Minimum0
Maximum175375
Zeros6148
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:42.277279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median314
Q31745.75
95-th percentile5968.75
Maximum175375
Range175375
Interquartile range (IQR)1745.75

Descriptive statistics

Standard deviation3247.786255
Coefficient of variation (CV)2.320240265
Kurtosis653.7344073
Mean1399.762906
Median Absolute Deviation (MAD)314
Skewness16.58798968
Sum20444937
Variance10548115.56
MonotonicityNot monotonic
2022-04-05T15:22:42.907852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06148
42.1%
813
 
0.1%
113
 
0.1%
711
 
0.1%
45311
 
0.1%
5249
 
0.1%
4209
 
0.1%
349
 
0.1%
3109
 
0.1%
3319
 
0.1%
Other values (4208)8365
57.3%
ValueCountFrequency (%)
06148
42.1%
113
 
0.1%
29
 
0.1%
35
 
< 0.1%
48
 
0.1%
57
 
< 0.1%
65
 
< 0.1%
711
 
0.1%
813
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
1753751
< 0.1%
791271
< 0.1%
701801
< 0.1%
666431
< 0.1%
639691
< 0.1%
594601
< 0.1%
516041
< 0.1%
513361
< 0.1%
501061
< 0.1%
464911
< 0.1%

forecast_discount_energy
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.966726003
Minimum0
Maximum30
Zeros14094
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:43.480553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.108288697
Coefficient of variation (CV)5.284112231
Kurtosis24.85471185
Mean0.966726003
Median Absolute Deviation (MAD)0
Skewness5.155098289
Sum14120
Variance26.09461341
MonotonicityNot monotonic
2022-04-05T15:22:43.921415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
014094
96.5%
30260
 
1.8%
28102
 
0.7%
2483
 
0.6%
2247
 
0.3%
257
 
< 0.1%
265
 
< 0.1%
192
 
< 0.1%
172
 
< 0.1%
232
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
014094
96.5%
51
 
< 0.1%
101
 
< 0.1%
172
 
< 0.1%
192
 
< 0.1%
2247
 
0.3%
232
 
< 0.1%
2483
 
0.6%
257
 
< 0.1%
265
 
< 0.1%
ValueCountFrequency (%)
30260
1.8%
28102
 
0.7%
265
 
< 0.1%
257
 
< 0.1%
2483
 
0.6%
232
 
< 0.1%
2247
 
0.3%
192
 
< 0.1%
172
 
< 0.1%
101
 
< 0.1%

forecast_meter_rent_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3528
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.08687115
Minimum0
Maximum599.31
Zeros725
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:44.503805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3325
Q116.18
median18.795
Q3131.03
95-th percentile145.72
Maximum599.31
Range599.31
Interquartile range (IQR)114.85

Descriptive statistics

Standard deviation66.16578283
Coefficient of variation (CV)1.048804317
Kurtosis4.491521412
Mean63.08687115
Median Absolute Deviation (MAD)9.315
Skewness1.505147852
Sum921446.84
Variance4377.910818
MonotonicityNot monotonic
2022-04-05T15:22:45.126452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0725
 
5.0%
131.76238
 
1.6%
18.32162
 
1.1%
18.37131
 
0.9%
129.61103
 
0.7%
15.98100
 
0.7%
131.3878
 
0.5%
18.4777
 
0.5%
16.2676
 
0.5%
18.4274
 
0.5%
Other values (3518)12842
87.9%
ValueCountFrequency (%)
0725
5.0%
0.091
 
< 0.1%
0.181
 
< 0.1%
0.241
 
< 0.1%
0.271
 
< 0.1%
0.311
 
< 0.1%
0.331
 
< 0.1%
0.342
 
< 0.1%
0.352
 
< 0.1%
0.366
 
< 0.1%
ValueCountFrequency (%)
599.315
< 0.1%
585.621
 
< 0.1%
562.131
 
< 0.1%
552.91
 
< 0.1%
548.411
 
< 0.1%
439.671
 
< 0.1%
434.041
 
< 0.1%
407.981
 
< 0.1%
407.979
0.1%
406.451
 
< 0.1%

forecast_price_energy_off_peak
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct516
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1372832657
Minimum0
Maximum0.273963
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:45.783260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11293
Q10.11634
median0.143166
Q30.146348
95-th percentile0.166178
Maximum0.273963
Range0.273963
Interquartile range (IQR)0.030008

Descriptive statistics

Standard deviation0.02462286235
Coefficient of variation (CV)0.1793580756
Kurtosis8.36453864
Mean0.1372832657
Median Absolute Deviation (MAD)0.019738
Skewness-0.1195860247
Sum2005.159379
Variance0.0006062853503
MonotonicityNot monotonic
2022-04-05T15:22:46.382566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.145711933
 
6.4%
0.144902732
 
5.0%
0.115174726
 
5.0%
0.146694644
 
4.4%
0.115237595
 
4.1%
0.11691449
 
3.1%
0.1169406
 
2.8%
0.146348339
 
2.3%
0.143166335
 
2.3%
0.116509308
 
2.1%
Other values (506)9139
62.6%
ValueCountFrequency (%)
022
 
0.2%
0.000666
0.5%
0.0009016
 
< 0.1%
0.09245373
0.5%
0.0944861
 
< 0.1%
0.0950222
 
< 0.1%
0.0950611
 
< 0.1%
0.0955581
 
< 0.1%
0.0959191
 
< 0.1%
0.0960951
 
< 0.1%
ValueCountFrequency (%)
0.27396318
0.1%
0.2739572
 
< 0.1%
0.27298121
0.1%
0.2729721
 
< 0.1%
0.2459268
 
0.1%
0.2453476
 
< 0.1%
0.23777611
 
0.1%
0.2367944
 
< 0.1%
0.2362913
 
< 0.1%
0.22927234
0.2%

forecast_price_energy_peak
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct329
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05049076722
Minimum0
Maximum0.195975
Zeros7021
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:47.040797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.084138
Q30.098837
95-th percentile0.10175
Maximum0.195975
Range0.195975
Interquartile range (IQR)0.098837

Descriptive statistics

Standard deviation0.04903650731
Coefficient of variation (CV)0.9711975081
Kurtosis-1.890754679
Mean0.05049076722
Median Absolute Deviation (MAD)0.0311955
Skewness-0.01433142786
Sum737.468146
Variance0.002404579049
MonotonicityNot monotonic
2022-04-05T15:22:47.709822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07021
48.1%
0.098837722
 
4.9%
0.100123596
 
4.1%
0.100015473
 
3.2%
0.100572445
 
3.0%
0.101397308
 
2.1%
0.103487288
 
2.0%
0.087381169
 
1.2%
0.086803159
 
1.1%
0.099419153
 
1.0%
Other values (319)4272
29.2%
ValueCountFrequency (%)
07021
48.1%
0.0765921
 
< 0.1%
0.0771241
 
< 0.1%
0.0781251
 
< 0.1%
0.0786412
 
< 0.1%
0.0788591
 
< 0.1%
0.0792212
 
< 0.1%
0.0792812
 
< 0.1%
0.0797711
 
< 0.1%
0.0797991
 
< 0.1%
ValueCountFrequency (%)
0.1959751
 
< 0.1%
0.1680926
 
< 0.1%
0.1680327
 
< 0.1%
0.1466768
 
0.1%
0.13633659
0.4%
0.136081
 
< 0.1%
0.1357615
 
< 0.1%
0.13573235
0.2%
0.1351823
 
< 0.1%
0.1346042
 
< 0.1%

forecast_price_pow_off_peak
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.13005553
Minimum0
Maximum59.26637796
Zeros94
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:48.389710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.606701
Q140.606701
median44.31137796
Q344.31137796
95-th percentile46.30537836
Maximum59.26637796
Range59.26637796
Interquartile range (IQR)3.70467696

Descriptive statistics

Standard deviation4.485988222
Coefficient of variation (CV)0.1040107222
Kurtosis54.70804128
Mean43.13005553
Median Absolute Deviation (MAD)0.9969996
Skewness-4.998771994
Sum629957.591
Variance20.12409033
MonotonicityNot monotonic
2022-04-05T15:22:48.967842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
44.311377966933
47.5%
40.6067014651
31.8%
45.80687796697
 
4.8%
46.30537836615
 
4.2%
45.30837756419
 
2.9%
41.1052014302
 
2.1%
40.9390266237
 
1.6%
41.2713642160
 
1.1%
58.99595196118
 
0.8%
094
 
0.6%
Other values (31)380
 
2.6%
ValueCountFrequency (%)
094
 
0.6%
35.555767921
 
< 0.1%
37.9292942
 
< 0.1%
40.6067014651
31.8%
40.72888531
 
0.2%
40.9390266237
 
1.6%
41.1052014302
 
2.1%
41.10670141
 
< 0.1%
41.2713642160
 
1.1%
41.27186821
 
< 0.1%
ValueCountFrequency (%)
59.2663779642
 
0.3%
59.1734679676
0.5%
59.051283961
 
< 0.1%
58.99595196118
0.8%
53.2843779614
 
0.1%
47.800878368
 
0.1%
47.306877962
 
< 0.1%
47.3023779610
 
0.1%
46.806877561
 
< 0.1%
46.803877566
 
< 0.1%

has_gas
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
False
11955 
True
2651 
ValueCountFrequency (%)
False11955
81.8%
True2651
 
18.2%
2022-04-05T15:22:49.385743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

imp_cons
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7752
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.7868958
Minimum0
Maximum15042.79
Zeros6169
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:49.783158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.395
Q3193.98
95-th percentile638.8175
Maximum15042.79
Range15042.79
Interquartile range (IQR)193.98

Descriptive statistics

Standard deviation341.3693656
Coefficient of variation (CV)2.23428432
Kurtosis380.893698
Mean152.7868958
Median Absolute Deviation (MAD)37.395
Skewness13.19879897
Sum2231605.4
Variance116533.0438
MonotonicityNot monotonic
2022-04-05T15:22:50.411414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06169
42.2%
0.35
 
< 0.1%
0.15
 
< 0.1%
0.154
 
< 0.1%
34.534
 
< 0.1%
26.264
 
< 0.1%
117.184
 
< 0.1%
26.514
 
< 0.1%
126.64
 
< 0.1%
42.044
 
< 0.1%
Other values (7742)8399
57.5%
ValueCountFrequency (%)
06169
42.2%
0.061
 
< 0.1%
0.092
 
< 0.1%
0.15
 
< 0.1%
0.141
 
< 0.1%
0.154
 
< 0.1%
0.172
 
< 0.1%
0.241
 
< 0.1%
0.271
 
< 0.1%
0.282
 
< 0.1%
ValueCountFrequency (%)
15042.791
< 0.1%
9682.891
< 0.1%
8732.61
< 0.1%
8254.161
< 0.1%
6787.121
< 0.1%
5836.491
< 0.1%
5343.761
< 0.1%
5311.971
< 0.1%
5019.251
< 0.1%
4925.361
< 0.1%

margin_gross_pow_ele
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.56512118
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:51.417578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.23117183
Coefficient of variation (CV)0.823573052
Kurtosis35.89260721
Mean24.56512118
Median Absolute Deviation (MAD)8.12
Skewness4.472632135
Sum358798.16
Variance409.3003134
MonotonicityNot monotonic
2022-04-05T15:22:52.053548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04258
 
1.8%
33.12238
 
1.6%
29.76170
 
1.2%
34.68161
 
1.1%
0157
 
1.1%
23.76156
 
1.1%
16.92156
 
1.1%
10.08151
 
1.0%
19.2141
 
1.0%
14.64135
 
0.9%
Other values (2381)12883
88.2%
ValueCountFrequency (%)
0157
1.1%
0.031
 
< 0.1%
0.12125
0.9%
0.2416
 
0.1%
0.367
 
< 0.1%
0.481
 
< 0.1%
0.61
 
< 0.1%
0.647
 
< 0.1%
0.661
 
< 0.1%
0.683
 
< 0.1%
ValueCountFrequency (%)
374.641
< 0.1%
314.761
< 0.1%
299.641
< 0.1%
248.641
< 0.1%
225.122
< 0.1%
224.891
< 0.1%
224.641
< 0.1%
219.881
< 0.1%
214.351
< 0.1%
214.141
< 0.1%

margin_net_pow_ele
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.56251746
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:52.735672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.23027977
Coefficient of variation (CV)0.8236240364
Kurtosis35.90123205
Mean24.56251746
Median Absolute Deviation (MAD)8.12
Skewness4.473325822
Sum358760.13
Variance409.2642197
MonotonicityNot monotonic
2022-04-05T15:22:53.372677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04258
 
1.8%
33.12238
 
1.6%
29.76170
 
1.2%
34.68161
 
1.1%
0157
 
1.1%
23.76156
 
1.1%
16.92156
 
1.1%
10.08151
 
1.0%
19.2141
 
1.0%
14.64135
 
0.9%
Other values (2381)12883
88.2%
ValueCountFrequency (%)
0157
1.1%
0.031
 
< 0.1%
0.12125
0.9%
0.2416
 
0.1%
0.367
 
< 0.1%
0.481
 
< 0.1%
0.61
 
< 0.1%
0.647
 
< 0.1%
0.661
 
< 0.1%
0.683
 
< 0.1%
ValueCountFrequency (%)
374.641
< 0.1%
314.761
< 0.1%
299.641
< 0.1%
248.641
< 0.1%
225.122
< 0.1%
224.891
< 0.1%
224.641
< 0.1%
219.881
< 0.1%
214.351
< 0.1%
214.141
< 0.1%

nb_prod_act
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.292345611
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:53.937325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum32
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7097735191
Coefficient of variation (CV)0.5492133937
Kurtosis258.9572461
Mean1.292345611
Median Absolute Deviation (MAD)0
Skewness8.636877937
Sum18876
Variance0.5037784484
MonotonicityNot monotonic
2022-04-05T15:22:54.367787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
111431
78.3%
22445
 
16.7%
3523
 
3.6%
4150
 
1.0%
531
 
0.2%
911
 
0.1%
68
 
0.1%
84
 
< 0.1%
102
 
< 0.1%
321
 
< 0.1%
ValueCountFrequency (%)
111431
78.3%
22445
 
16.7%
3523
 
3.6%
4150
 
1.0%
531
 
0.2%
68
 
0.1%
84
 
< 0.1%
911
 
0.1%
102
 
< 0.1%
321
 
< 0.1%
ValueCountFrequency (%)
321
 
< 0.1%
102
 
< 0.1%
911
 
0.1%
84
 
< 0.1%
68
 
0.1%
531
 
0.2%
4150
 
1.0%
3523
 
3.6%
22445
 
16.7%
111431
78.3%

net_margin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct11965
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.2645221
Minimum0
Maximum24570.65
Zeros185
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:54.937019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.16
Q150.7125
median112.53
Q3243.0975
95-th percentile587.685
Maximum24570.65
Range24570.65
Interquartile range (IQR)192.385

Descriptive statistics

Standard deviation311.7981301
Coefficient of variation (CV)1.647419847
Kurtosis2642.965291
Mean189.2645221
Median Absolute Deviation (MAD)75.32
Skewness36.56951466
Sum2764397.61
Variance97218.07391
MonotonicityNot monotonic
2022-04-05T15:22:55.593285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0185
 
1.3%
0.496
 
< 0.1%
57.375
 
< 0.1%
0.015
 
< 0.1%
56.335
 
< 0.1%
35.985
 
< 0.1%
86.525
 
< 0.1%
21.484
 
< 0.1%
55.384
 
< 0.1%
91.864
 
< 0.1%
Other values (11955)14378
98.4%
ValueCountFrequency (%)
0185
1.3%
0.015
 
< 0.1%
0.024
 
< 0.1%
0.033
 
< 0.1%
0.043
 
< 0.1%
0.053
 
< 0.1%
0.061
 
< 0.1%
0.071
 
< 0.1%
0.081
 
< 0.1%
0.092
 
< 0.1%
ValueCountFrequency (%)
24570.651
< 0.1%
10203.51
< 0.1%
4346.371
< 0.1%
4305.791
< 0.1%
3768.161
< 0.1%
3407.651
< 0.1%
3403.271
< 0.1%
3323.021
< 0.1%
3215.031
< 0.1%
2711.191
< 0.1%

num_years_antig
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.99780912
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:56.115968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.611749267
Coefficient of variation (CV)0.3224911613
Kurtosis4.07814946
Mean4.99780912
Median Absolute Deviation (MAD)1
Skewness1.446213823
Sum72998
Variance2.5977357
MonotonicityNot monotonic
2022-04-05T15:22:56.588954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
64769
32.7%
43982
27.3%
32433
16.7%
52317
15.9%
7509
 
3.5%
11185
 
1.3%
12110
 
0.8%
8103
 
0.7%
992
 
0.6%
1081
 
0.6%
Other values (3)25
 
0.2%
ValueCountFrequency (%)
11
 
< 0.1%
211
 
0.1%
32433
16.7%
43982
27.3%
52317
15.9%
64769
32.7%
7509
 
3.5%
8103
 
0.7%
992
 
0.6%
1081
 
0.6%
ValueCountFrequency (%)
1313
 
0.1%
12110
 
0.8%
11185
 
1.3%
1081
 
0.6%
992
 
0.6%
8103
 
0.7%
7509
 
3.5%
64769
32.7%
52317
15.9%
43982
27.3%

origin_up
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
lxidpiddsbxsbosboudacockeimpuepw
7097 
kamkkxfxxuwbdslkwifmmcsiusiuosws
4294 
ldkssxwpmemidmecebumciepifcamkci
3148 
MISSING
 
64
usapbepcfoloekilkwsdiboslwaxobdp
 
2

Length

Max length32
Median length32
Mean length31.89045598
Min length7

Characters and Unicode

Total characters465792
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowlxidpiddsbxsbosboudacockeimpuepw
2nd rowkamkkxfxxuwbdslkwifmmcsiusiuosws
3rd rowkamkkxfxxuwbdslkwifmmcsiusiuosws
4th rowkamkkxfxxuwbdslkwifmmcsiusiuosws
5th rowkamkkxfxxuwbdslkwifmmcsiusiuosws

Common Values

ValueCountFrequency (%)
lxidpiddsbxsbosboudacockeimpuepw7097
48.6%
kamkkxfxxuwbdslkwifmmcsiusiuosws4294
29.4%
ldkssxwpmemidmecebumciepifcamkci3148
21.6%
MISSING64
 
0.4%
usapbepcfoloekilkwsdiboslwaxobdp2
 
< 0.1%
ewxeelcelemmiwuafmddpobolfuxioce1
 
< 0.1%

Length

2022-04-05T15:22:57.087792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-05T15:22:57.431360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
lxidpiddsbxsbosboudacockeimpuepw7097
48.6%
kamkkxfxxuwbdslkwifmmcsiusiuosws4294
29.4%
ldkssxwpmemidmecebumciepifcamkci3148
21.6%
missing64
 
0.4%
usapbepcfoloekilkwsdiboslwaxobdp2
 
< 0.1%
ewxeelcelemmiwuafmddpobolfuxioce1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s49063
10.5%
i46771
10.0%
d38984
 
8.4%
m35722
 
7.7%
c31084
 
6.7%
k30573
 
6.6%
x30228
 
6.5%
u30228
 
6.5%
b28740
 
6.2%
p27594
 
5.9%
Other values (11)116805
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter465344
99.9%
Uppercase Letter448
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s49063
10.5%
i46771
10.1%
d38984
 
8.4%
m35722
 
7.7%
c31084
 
6.7%
k30573
 
6.6%
x30228
 
6.5%
u30228
 
6.5%
b28740
 
6.2%
p27594
 
5.9%
Other values (6)116357
25.0%
Uppercase Letter
ValueCountFrequency (%)
I128
28.6%
S128
28.6%
N64
14.3%
M64
14.3%
G64
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin465792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s49063
10.5%
i46771
10.0%
d38984
 
8.4%
m35722
 
7.7%
c31084
 
6.7%
k30573
 
6.6%
x30228
 
6.5%
u30228
 
6.5%
b28740
 
6.2%
p27594
 
5.9%
Other values (11)116805
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII465792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s49063
10.5%
i46771
10.0%
d38984
 
8.4%
m35722
 
7.7%
c31084
 
6.7%
k30573
 
6.6%
x30228
 
6.5%
u30228
 
6.5%
b28740
 
6.2%
p27594
 
5.9%
Other values (11)116805
25.1%

pow_max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct698
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.13513563
Minimum3.3
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2022-04-05T15:22:58.023032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile10.39
Q112.5
median13.856
Q319.1725
95-th percentile41.5
Maximum320
Range316.7
Interquartile range (IQR)6.6725

Descriptive statistics

Standard deviation13.53474337
Coefficient of variation (CV)0.7463271105
Kurtosis59.20256296
Mean18.13513563
Median Absolute Deviation (MAD)3.056
Skewness5.786784914
Sum264881.791
Variance183.1892782
MonotonicityNot monotonic
2022-04-05T15:22:58.635478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.22124
 
14.5%
10.3922000
 
13.7%
13.8561504
 
10.3%
15583
 
4.0%
10.35480
 
3.3%
19.8416
 
2.8%
16.5405
 
2.8%
20294
 
2.0%
12.5269
 
1.8%
13.15234
 
1.6%
Other values (688)6297
43.1%
ValueCountFrequency (%)
3.33
< 0.1%
3.4641
 
< 0.1%
41
 
< 0.1%
52
< 0.1%
5.1962
< 0.1%
5.752
< 0.1%
62
< 0.1%
6.91
 
< 0.1%
6.9284
< 0.1%
7.71
 
< 0.1%
ValueCountFrequency (%)
3201
 
< 0.1%
2601
 
< 0.1%
2004
< 0.1%
1921
 
< 0.1%
1802
< 0.1%
1661
 
< 0.1%
1641
 
< 0.1%
1602
< 0.1%
155.881
 
< 0.1%
1553
< 0.1%

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
0
13187 
1
1419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14606
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Length

2022-04-05T15:22:59.221222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-05T15:22:59.539230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Most occurring characters

ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14606
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common14606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII14606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013187
90.3%
11419
 
9.7%

Interactions

2022-04-05T15:22:14.773028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:30.628999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:41.147455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:51.061711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:01.726578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:11.646740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:21.960203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:32.385503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:42.447455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:52.803337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:03.530299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:13.891007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:24.059555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:34.610549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:45.164387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:22:15.351968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:31.737070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:41.703406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:51.675532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:02.291077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:12.246925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:22.544540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:32.972520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:43.035709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:53.427257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:04.113652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:14.485402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:21:35.211166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:45.738136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:19:32.289584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:22:06.173868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:22:16.515417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:32.933180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:42.842355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:52.931368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:20:55.274123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:21:57.283683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:22:07.315131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:22:17.648117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:34.091938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:43.966488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:54.155017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:04.513603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:14.650697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:24.879761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:35.336898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:45.358927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:55.930413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:06.770870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:16.859804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:21:37.590280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:19:54.773452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:05.068632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:15.255268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:20:35.937762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:45.936653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:56.555868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:07.368295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:21:48.625545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:21:58.450313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:22:08.480772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:22:18.803440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T15:19:45.086782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:19:55.393830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:05.625360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T15:20:15.858828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2022-04-05T15:22:59.919047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-05T15:23:01.145451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-05T15:23:02.359608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-05T15:23:03.807992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-05T15:23:04.447529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-05T15:22:25.672852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-05T15:22:30.923265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idchannel_salescons_12mcons_gas_12mcons_last_monthdate_activdate_enddate_modif_proddate_renewalforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakhas_gasimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxchurn
024011ae4ebbe3035111d65fa7c15bc57foosdfpfkusacimwkcsosbicdxkicaua05494602013-06-152016-06-152015-11-012015-06-230.0000.01.780.1144810.09814240.606701t0.0025.4425.442678.993lxidpiddsbxsbosboudacockeimpuepw43.6481
1d29c2c54acc38ff3c0614d0a653813ddMISSING4660002009-08-212016-08-302009-08-212015-08-31189.9500.016.270.1457110.00000044.311378f0.0016.3816.38118.896kamkkxfxxuwbdslkwifmmcsiusiuosws13.8000
2764c75f661154dac3a6c254cd082ea7dfoosdfpfkusacimwkcsosbicdxkicaua544002010-04-162016-04-162010-04-162015-04-1747.9600.038.720.1657940.08789944.311378f0.0028.6028.6016.606kamkkxfxxuwbdslkwifmmcsiusiuosws13.8560
3bba03439a292a1e166f80264c16191cblmkebamcaaclubfxadlmueccxoimlema1584002010-03-302016-03-302010-03-302015-03-31240.0400.019.830.1466940.00000044.311378f0.0030.2230.22125.466kamkkxfxxuwbdslkwifmmcsiusiuosws13.2000
4149d57cf92fc41cf94415803a877cb4bMISSING442505262010-01-132016-03-072010-01-132015-03-09445.755260.0131.730.1169000.10001540.606701f52.3244.9144.91147.986kamkkxfxxuwbdslkwifmmcsiusiuosws19.8000
51aa498825382410b098937d65c4ec26dusilxuppasemubllopkaafesmlibmsdf8302019982011-12-092016-12-092015-11-012015-12-10796.9419980.030.120.1647750.08613145.308378f181.2133.1233.121118.894lxidpiddsbxsbosboudacockeimpuepw13.2001
67ab4bf4878d8f7661dfc20e9b8e18011foosdfpfkusacimwkcsosbicdxkicaua45097002011-12-022016-12-022011-12-022015-12-038069.2800.00.000.1661780.08753844.311378f0.004.044.041346.634lxidpiddsbxsbosboudacockeimpuepw15.0001
701495c955be7ec5e7f3203406785aae0foosdfpfkusacimwkcsosbicdxkicaua29552012602010-04-212016-04-212010-04-212015-04-22864.737510.0144.490.1151740.09883740.606701f70.6353.9253.921100.096lxidpiddsbxsbosboudacockeimpuepw26.4000
8f53a254b1115634330c12c7fdbf7958ausilxuppasemubllopkaafesmlibmsdf2962002011-09-232016-09-232011-09-232015-09-25444.3800.015.850.1457110.00000044.311378f0.0012.8212.82142.594kamkkxfxxuwbdslkwifmmcsiusiuosws13.2000
910c1b2f97a2d2a6f10299dc213d1a370lmkebamcaaclubfxadlmueccxoimlema26064021882010-05-042016-05-042015-04-292015-05-052738.1021880.0130.430.1157610.09941940.606701f219.5933.4233.421329.606lxidpiddsbxsbosboudacockeimpuepw31.5000

Last rows

idchannel_salescons_12mcons_gas_12mcons_last_monthdate_activdate_enddate_modif_proddate_renewalforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakhas_gasimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxchurn
14596c3f4f737d598a1b47a94440bb18c3c06lmkebamcaaclubfxadlmueccxoimlema1097002011-02-092016-02-092011-02-092015-02-11165.6000.016.040.1466940.00000044.311378f0.0026.0426.04117.385ldkssxwpmemidmecebumciepifcamkci10.3920
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